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Transcatheter Self‐Powered Ultrasensitive Endocardial Pressure Sensor
Abstract
Changes in endocardial pressure (EP) have important clinical significance for heart failure patients with impaired cardiac function. As a vital parameter for evaluating cardiac function, EP is commonly monitored by invasive and expensive cardiac catheterization, which is not feasible for long‐term and continuous data collection. In this work, a miniaturized, flexible, and self‐powered endocardial pressure sensor (SEPS) based on triboelectric nanogenerator (TENG), which is integrated with a surgical catheter for minimally invasive implantation, is reported. In a porcine model, SEPS is implanted into the left ventricle and the left atrium. The SEPS has a good response both in low‐ and high‐pressure environments. The SEPS achieves the ultrasensitivity, real‐time monitoring, and mechanical stability in vivo. An excellent linearity ( R 2 = 0.997) with a sensitivity of 1.195 mV mmHg −1 is obtained. Furthermore, cardiac arrhythmias such as ventricular fibrillation and ventricular premature contraction can also be detected by SEPS. The device may promote the development of miniature implantable medical sensors for monitoring and diagnosis of cardiovascular diseases.
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In the citing paper
“Figure 3b, (Liu et al. 2019) presents a novel approach to monitoring intracardiac pressure using a self-powered pressure sensor (SEPS) that is integrated with a minimally invasive implanted heart catheter.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Liu et al. 2019
In the citing paper
“The SEPS demonstrated its feasibility through in vitro and in vivo experiments, addressing the limitations of traditional invasive monitoring methods by providing a minimally invasive and self-powered solution.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Liu et al. 2019
In the citing paper
“After optimization exploration, SEPS achieved a sensitivity of 1.195 mV/mmHg with a linearity of R2=0.997.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Liu et al. 2019
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Self-citation
Metadata
- Authors
- Liu, Z., Ma, Y., Ouyang, H., Shi, B., Li, N., Jiang, D., Xie, F., Qu, D., Zou, Y., Huang, Y., Li, H., Zhao, C., Tan, P., Yu, M., Fan, Y., Zhang, H., Wang, Z. L., & Li, Z.
- Affiliations
- Beihang University (CN)
- Second Military Medical University (CN)
- Chinese Academy of Sciences (CN)
- National Research Center for Rehabilitation Technical Aids (CN)
- Georgia Institute of Technology (US)
- Corresponding author
- Fan, Yubo
- Zhang, Hao
- Wang, Zhong Lin
- Li, Zhou
- ORCID
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2018-11-23
- Volume
- 29
- Issue
- 3
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W2901061669
- Cited by
- 308
- References
- 35
- Citations by year
- 2026: 13 · 2025: 31 · 2024: 46 · 2023: 48 · 2022: 50 · 2021: 48 · 2020: 47 · 2019: 24 · 2018: 1
- FWCI
- 11.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advanced Functional Materials (publishedVersion)
- Funders
- National Natural Science Foundation of China (61875015, 31571006, 11421202, 81601629, 61501039, 81800443, 21801019)
- Natural Science Foundation of Beijing Municipality (2182091, 2162017)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- triboelectric nanogenerator, self-powered sensors, biomedical implants, cardiac function monitoring, heart failure, flexible sensors, arrhythmia detection, ventricular fibrillation, premature ventricular contractions, porcine model
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